{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "interracial-abuse",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "prescription-cleaners",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat1 = torch.tensor([\n",
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    "])\n",
    "mat2 = torch.tensor([\n",
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    "    [0.4789169714645932, 0.9991625788242381, 0.5032253037003531, 0.5011164651342566, 0.4993235918821818, 0.5043570032037765, 0.5039307455344658, 0.5010185142810818, 0.5002840495455224, 0.5013934441389714, 0.4978271022423261, 0.5026430544549129, 0.49857430519766316, 0.49847140407086626, 0.49992126846443136, 0.4992373691031389, 0.5015855156254266, 0.5003059348952016, 0.5015210209187555, 0.501029454434541, 0.5001611847561879, 0.5016427526575022, 0.49783765857898366, 0.4991234011825123, 0.5001881446221728, 0.5039992527664084, 0.5005007581709843, 0.49836965200838207, 0.49627419501480735, 0.5006995324461605, 0.4975509912974351, 0.5000186232549615, 0.5024190813195707, 0.5004831019738532, 0.49946265704323584, 0.5009313047578127, 0.5021190892144439, 0.49885546196700636, 0.49886839426812013, 0.49730383557621993, 0.5030175036763567, 0.4978141518398324, 0.500330653838793, 0.5020926085085804, 0.5010036298370714, 0.4995106918871082, 0.49938305659638643, 0.5022492292576859, 0.5006890865082603, 0.4951210882291729, 0.4987475145755792],\n",
    "    [0.47195334463462313, 0.13867604463091848, 0.5034006862345214, 0.5010810489449838, 0.49980358763095273, 0.5030748597335857, 0.504011015000157, 0.5011837447663677, 0.5002375004080809, 0.5020000263888594, 0.4978118773394127, 0.5012071132153779, 0.4987916278430785, 0.4985267545880176, 0.4998848213799886, 0.49922466642933266, 0.5016034213848383, 0.5003051588939819, 0.5012590304351185, 0.5002445247868915, 0.5001222142334409, 0.5016185522650939, 0.4978793659194122, 0.49910396807224067, 0.5001422372346541, 0.5039862197112269, 0.5003050406446206, 0.49840776829200495, 0.49627309848333917, 0.5007494658940392, 0.49768246963480944, 0.5000297990789222, 0.5024114487493209, 0.49977699849705715, 0.5014653703879793, 0.5009520171844762, 0.5021275707092893, 0.4988749381914535, 0.4988736804764574, 0.49730305988150475, 0.503024620227776, 0.4977537936770528, 0.5003665294801374, 0.502328625055765, 0.5010088982652922, 0.49939236074129595, 0.5000096141153275, 0.5019605680215811, 0.50068019663912, 0.4949390359661735, 0.49874569112358463],\n",
    "    [0.5568440870525213, 0.42868006549448323, 0.5036592880872294, 0.5010733794683832, 0.4993202561440845, 0.5041748617976317, 0.5040794211626298, 0.5010192049972054, 0.5002515271935875, 0.5014037449296452, 0.4978768985493487, 0.5011481965337488, 0.4986073376407691, 0.4986025776908344, 0.5004204798644124, 0.4992613549620576, 0.5015840420809571, 0.5003024438936488, 0.5015694500874831, 0.5010325534300428, 0.4994715337211319, 0.5016463942927007, 0.49795702629008265, 0.4991225828392999, 0.49997107848859, 0.5040049290492074, 0.5004944292891031, 0.4983178413111106, 0.496272400253917, 0.5006949331847593, 0.4975529221907584, 0.5000301834436142, 0.5024121628620617, 0.5004721929850203, 0.5013761886513346, 0.5009381600861506, 0.5021200272960712, 0.4988567705245513, 0.49910666632832884, 0.49730312102654767, 0.5030116024728973, 0.49781302622706675, 0.500329985580515, 0.5021289675101861, 0.5010061451429795, 0.4995260634118772, 0.49940786340112875, 0.5019554299131729, 0.5006840437373468, 0.495113576419055, 0.4988843693289454],\n",
    "    [0.5428404700529856, 0.31824015309811793, 0.5032376429065484, 0.5010265373144802, 0.49931268115694377, 0.5030576478825042, 0.5039358758006305, 0.5009967297858051, 0.5002542021244618, 0.501405318568569, 0.4978795007605655, 0.5026477770747069, 0.498460986271094, 0.4986065328923772, 0.49997964166491066, 0.4992340814839499, 0.5016057054361455, 0.5003027144549987, 0.5012692171898376, 0.5009397852261981, 0.5002422008564971, 0.5015667034161414, 0.49781246144559904, 0.4991234665283832, 0.49998483452845244, 0.5040020210009514, 0.5001965703550724, 0.4983705043387409, 0.4962660859059872, 0.5007158749545058, 0.49756740367022984, 0.5000332735943006, 0.5024192443132088, 0.49971004788280143, 0.5013728096329076, 0.5009312467494766, 0.502115477379043, 0.49885462423436006, 0.4990694504206433, 0.49730612803685914, 0.5030247683885206, 0.4977575751510065, 0.5003212102846522, 0.5020923393225626, 0.5009763354073911, 0.49947595689766344, 0.4993830021744798, 0.5019809123888067, 0.5006865691578857, 0.49493897201787285, 0.49888284206663713],\n",
    "    [0.4677938462262745, 0.002089452165050021, 0.5036435964581921, 0.5010756612074644, 0.4993235918821818, 0.5043578842699231, 0.5039307096568956, 0.5010184810923191, 0.5002846403786203, 0.5020239395963435, 0.49782694171898817, 0.5026837054922779, 0.49846753307300423, 0.4985067865761871, 0.49992126846443136, 0.4992373691031389, 0.5015858819143183, 0.5003059348952016, 0.501553695125447, 0.501029454434541, 0.5001227692950064, 0.5016491575457478, 0.49783765857898366, 0.499121155672852, 0.5002203162775778, 0.5039992527664084, 0.5005007472620464, 0.4984123163606694, 0.49627419501480735, 0.5006998046516318, 0.49767369779459397, 0.5000180792154919, 0.5024189967394462, 0.5004831019738532, 0.5014400542108114, 0.5009371421561157, 0.5021190892144439, 0.49887522415936764, 0.4988677213899331, 0.4973038669925168, 0.5030256782011636, 0.4978101900835099, 0.500328733914378, 0.5020926085085804, 0.5009742684004345, 0.4995106918871082, 0.4999237762162455, 0.5018935478196828, 0.5006891286045634, 0.495106225870003, 0.4988780235984669],\n",
    "    [0.5873195617520243, 0.5494668199385657, 0.5034033775262191, 0.5010247927958081, 0.49983879650703933, 0.5029195576307524, 0.5037946070858367, 0.5009954712346968, 0.5003579930108589, 0.5019943585248225, 0.49827186703765713, 0.5011094641995292, 0.4985762240734018, 0.49939871498499433, 0.4999460574179477, 0.49965310869993734, 0.5016079506630845, 0.5003059278244502, 0.5010207751804897, 0.5009390036026184, 0.500142200567855, 0.5016431608971093, 0.49779755290558897, 0.49911520425294204, 0.49997670135252087, 0.5039819946373404, 0.500513295240184, 0.49837081902453867, 0.4962743361552522, 0.5006979826224746, 0.49768586970488227, 0.5000186232549615, 0.5024091616317368, 0.499731932182262, 0.5013227563810323, 0.5009985338625884, 0.5021190892144439, 0.4989460152310557, 0.4990699393612602, 0.4973079146259462, 0.5030133262869265, 0.4978151565449397, 0.5003414583156859, 0.5021076072626249, 0.5010343460722861, 0.49952533278715555, 0.499375954190443, 0.5020314363201439, 0.5006851699067775, 0.49512011811132967, 0.4987475145755792],\n",
    "    [0.5341186247815572, 0.19574398615850103, 0.5033911907470056, 0.5010121208682604, 0.4997932478380801, 0.50441887398496, 0.5040695652370376, 0.5009687463037024, 0.5002740464795303, 0.5013235390914568, 0.49823237137091847, 0.5011846257881925, 0.4986218191106406, 0.4992788880296194, 0.49988257553643956, 0.499639545063359, 0.5016034261154136, 0.5003052295169181, 0.5012666587277953, 0.5009170473391376, 0.5001950094103643, 0.5016184643984958, 0.497880381484289, 0.49911390099142394, 0.5001427510328801, 0.503992541919555, 0.5001977235626179, 0.49840799410654635, 0.49630327972945104, 0.500706186999139, 0.4976830205830088, 0.5000292516294241, 0.502372070832654, 0.5004008442054816, 0.5013610348669911, 0.5009264634617557, 0.5021243178250185, 0.49894480916837686, 0.4990568325624512, 0.49730465056589207, 0.5030246459878267, 0.49472050153263664, 0.5003647647153495, 0.5025755713367808, 0.5010056398770467, 0.49947668313835514, 0.4993363840706056, 0.5019881885605592, 0.5006863706369741, 0.4949337154508526, 0.4987530386493374],\n",
    "    [0.584169377223502, 0.34219880563988153, 0.5036548213205123, 0.5010810489449838, 0.4998032215674805, 0.5030748597335857, 0.504011015000157, 0.5011837447663677, 0.5002375004080809, 0.5020000263888594, 0.4978119185031425, 0.5012071132153779, 0.4987916278430785, 0.498353013765868, 0.5004211813176154, 0.4992344926406645, 0.5016017042705041, 0.5003051588939819, 0.5015471145387905, 0.5002327554285508, 0.5001619634464717, 0.5016425585948321, 0.49781250990959763, 0.49910396807224067, 0.5001172031822395, 0.5039862132706219, 0.5003050406446206, 0.49840776829200495, 0.49627309848333917, 0.5007494658940392, 0.49754169364512985, 0.5000297983660962, 0.5024117748721416, 0.49976746174818143, 0.5014653703879793, 0.5009520171844762, 0.5021275084891493, 0.4988751787960331, 0.4988736804764574, 0.49730303539070836, 0.503024620227776, 0.4977538228754531, 0.5003665294801374, 0.502328625055765, 0.5010087575814185, 0.49939236074129595, 0.5000096141153275, 0.5019625211335242, 0.5006802198918191, 0.4949390359661735, 0.49874569112358463],\n",
    "    [0.6305304832214979, 0.4288276801942748, 0.5032597778498251, 0.5011217875383325, 0.49983779108977994, 0.5031060081421452, 0.5037730551574999, 0.5009644244249978, 0.5007296991318547, 0.5014290148941989, 0.4978146637550491, 0.5011692838662919, 0.49857368561668525, 0.49938990398589084, 0.5004017566326285, 0.4992623994640249, 0.5016022044170598, 0.5003051340071529, 0.5010186710447103, 0.5008927209066214, 0.5002428302556443, 0.5015983004017502, 0.4977993680855051, 0.498720702650374, 0.5001775533206165, 0.5042828269300842, 0.5002919096685793, 0.49840780897317716, 0.4962723754941264, 0.5007882426184959, 0.49769900697202013, 0.5000299428944703, 0.5023720384793184, 0.4997086885044099, 0.5015324427760773, 0.5009387515203264, 0.5021272479697906, 0.49894196312478784, 0.49904524372216863, 0.49730491843904195, 0.5029613333173205, 0.49775817385991983, 0.5003747435059758, 0.5023151421993473, 0.5010080839396568, 0.49951488690663176, 0.500049864846672, 0.5019005461115268, 0.5006814091846238, 0.494943500863229, 0.49874561809445417],\n",
    "    [0.653396424872756, 0.5956388476372603, 0.5036543154129147, 0.5010272008055784, 0.49984172793739856, 0.5030155493969402, 0.503935301253576, 0.5009918365954495, 0.5004822479971577, 0.5013194720478484, 0.49826003709430533, 0.5010643412699617, 0.49860912404562724, 0.4993629072578635, 0.4999686979571187, 0.49964308520234163, 0.5016066519750311, 0.5003025320011657, 0.5015295810306136, 0.500935852790652, 0.5002609133796043, 0.5015983624641754, 0.49778695783754895, 0.4991130408413205, 0.4999794137788609, 0.5042057003343869, 0.5005053987613463, 0.4983234603298641, 0.4961564282409456, 0.5007292489100326, 0.4974955316119303, 0.5002924736960286, 0.5024098966121093, 0.4997531184440218, 0.5014031014317093, 0.5009943599136792, 0.5021207023390107, 0.4989384282340633, 0.49906485832990716, 0.4973045033231971, 0.5030301069828672, 0.4979582556943588, 0.5003790243451416, 0.5023636306527915, 0.501004442269889, 0.4995260551671438, 0.5000283524803729, 0.5020068536439513, 0.500682863012566, 0.49494372145192206, 0.49874684758037063],\n",
    "    [0.6506484219811864, 0.5003015636167675, 0.5034041456438635, 0.5010453645256377, 0.4997770528163784, 0.504510549610615, 0.5039932219469955, 0.5010168737861583, 0.5002830747089507, 0.5013718308886834, 0.4978774304961476, 0.5026665214341709, 0.4985635943564129, 0.4998829138116538, 0.49993791740374965, 0.49962306980556503, 0.5016033776343556, 0.5003048067459767, 0.5015586558524989, 0.5009400289772491, 0.4995635858786941, 0.5016446405178553, 0.49778058856408447, 0.49912239893745625, 0.5002132679233008, 0.5042124377675921, 0.5005004918074957, 0.49832271060555783, 0.4962736500130154, 0.5007040781250208, 0.4976906791520745, 0.5002915967655898, 0.5024190933404604, 0.5003841691059204, 0.5013427033426925, 0.5009962093743027, 0.5021234102510609, 0.4989344325008692, 0.49906404194196097, 0.49728321565424105, 0.5030334393179259, 0.4977543513402511, 0.5000732065380442, 0.5023574629794357, 0.5010072621441625, 0.49951961979025616, 0.4999873079711729, 0.5022454369218162, 0.5001192332517722, 0.49495504875360447, 0.4987388152315194],\n",
    "    [0.6991164638687483, 0.4765711971531167, 0.5034129978951178, 0.5010210350150832, 0.49984020312348415, 0.5029676934195881, 0.5037686180996321, 0.5010167137411058, 0.500237875250287, 0.5013246513683515, 0.49786954803425854, 0.5012235936511498, 0.4984695388537236, 0.49939510849960933, 0.49993525336324934, 0.49915980166899976, 0.5016063906555746, 0.5003052308395634, 0.5015447614188275, 0.5009354582849103, 0.5001323721538077, 0.5016118918900858, 0.4978204585848745, 0.4991219628278413, 0.5000278101082772, 0.5039884094999022, 0.5003226162185921, 0.4983708204273047, 0.49624047437171265, 0.5007967916999693, 0.4979923752515945, 0.5000296785143562, 0.5024193215464972, 0.4997524001781054, 0.5013903537256514, 0.500948031656537, 0.5021170008407467, 0.4988595046140524, 0.4988750925330015, 0.4973032916858494, 0.5030303097372433, 0.497758408316495, 0.5000637922138087, 0.5021163594345581, 0.5010061576781498, 0.4995202672184717, 0.49939639180530493, 0.5018832227447275, 0.500685005404738, 0.49512295565626235, 0.4987473212291295],\n",
    "])\n",
    "\n",
    "# mat1 = mat1 * 2 - 1\n",
    "mat2 = mat2 * 2 - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sustained-stability",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "device = 'cpu'\n",
    "\n",
    "d1 = mat1.shape[1]\n",
    "d2 = mat2.shape[1]\n",
    "batch_size = mat1.shape[0]\n",
    "\n",
    "hDs = [(d1 + d2) // 5, (d1 + d2) // 2, (d1 + d2)]\n",
    "hGs = [(d1 + d2) // 5, (d1 + d2) // 2, (d1 + d2)]\n",
    "epochs = [3,5,8]\n",
    "glrs = [1e-1, 5e-2, 2e-2, 1e-2, 5e-3, 2e-3, 1e-3, 5e-4, 2e-4, 1e-4]\n",
    "dlrs = [1e-1, 5e-2, 2e-2, 1e-2, 5e-3, 2e-3, 1e-3, 5e-4, 2e-4, 1e-4]\n",
    "\n",
    "# hD = (d1 + d2) // 5\n",
    "# hG = (d1 + d2) // 5\n",
    "# num_epoch = 8\n",
    "# g_lr = 1e-4\n",
    "# d_lr = 1e-4\n",
    "\n",
    "for hD in hDs:\n",
    "    for hG in hGs:\n",
    "        for g_lr in glrs:\n",
    "            for d_lr in dlrs:\n",
    "                for num_epoch in epochs:\n",
    "                    for _ in range(3):\n",
    "                        \n",
    "                        class discriminator(nn.Module):\n",
    "                            def __init__(self):\n",
    "                                super().__init__()\n",
    "                                self.dis = nn.Sequential(\n",
    "                                    nn.Linear(d2, hD),\n",
    "                        #             nn.LeakyReLU(0.2),\n",
    "                                    nn.Linear(hD, hD),\n",
    "                                    nn.Linear(hD, 1),\n",
    "                                    nn.Sigmoid(),\n",
    "                                )\n",
    "\n",
    "                            def forward(self, x):\n",
    "                                x = self.dis(x)\n",
    "                                return x\n",
    "\n",
    "\n",
    "                        class generator(nn.Module):\n",
    "                            def __init__(self):\n",
    "                                super().__init__()\n",
    "                                self.gen = nn.Sequential(\n",
    "                                    nn.Linear(d1, hG),\n",
    "                        #             nn.ReLU(True),\n",
    "                                    nn.Linear(hG, hG),\n",
    "                                    nn.Linear(hG, d2),\n",
    "                                    nn.Tanh(),\n",
    "                                )\n",
    "\n",
    "                            def forward(self,x):\n",
    "                                x = self.gen(x)\n",
    "                                return x\n",
    "\n",
    "\n",
    "                        D = discriminator()\n",
    "                        G = generator()\n",
    "                        D = D.to(device)\n",
    "                        G = G.to(device)\n",
    "\n",
    "                        criterion = nn.BCELoss()\n",
    "                        d_optimizer = torch.optim.Adam(D.parameters(), d_lr)\n",
    "                        g_optimizer = torch.optim.Adam(G.parameters(), g_lr)\n",
    "\n",
    "                        for epoch in range(num_epoch):\n",
    "                            #-----------------Discriminator------------------\n",
    "                            real_feature = mat2.to(device)\n",
    "                            real_label = torch.ones(batch_size).to(device)\n",
    "                            real_out = D(real_feature).squeeze(1)\n",
    "                            d_loss_real = criterion(real_out, real_label)\n",
    "                            real_scores = real_out\n",
    "\n",
    "                            noise = torch.rand(batch_size, d2).to(device)\n",
    "                        #     noise = mat1.to(device)\n",
    "                            fake_feature = G(noise)\n",
    "                            fake_label = torch.zeros(batch_size).to(device)\n",
    "                            fake_out = D(fake_feature.detach()).squeeze(1)\n",
    "                            d_loss_fake = criterion(fake_out, fake_label)\n",
    "                            fake_scores = fake_out\n",
    "\n",
    "                            d_loss = d_loss_real + d_loss_fake\n",
    "                            d_optimizer.zero_grad()\n",
    "                            d_loss.backward()\n",
    "                            d_optimizer.step()\n",
    "\n",
    "                            #-------------------Generator------------------\n",
    "                            noise = torch.rand(batch_size, d2).to(device)\n",
    "                        #     nosie = mat1.to(device)\n",
    "                            fake_feature = G(noise)\n",
    "                            output = D(fake_feature).squeeze(1)\n",
    "                            g_loss = criterion(output, real_label)\n",
    "\n",
    "                            g_optimizer.zero_grad()\n",
    "                            g_loss.backward()\n",
    "                            g_optimizer.step()\n",
    "\n",
    "    #                         print('Epoch[{}/{}], d_loss: {:.6f}, g_loss: {:.6f} '\n",
    "    #                              'D real: {:.6f}, D fake: {:.6f}'.format(\n",
    "    #                                  epoch, num_epoch, d_loss.item(), g_loss.item(),\n",
    "    #                                  torch.mean(real_scores).item(), torch.mean(fake_scores).item()\n",
    "    #                              ))\n",
    "\n",
    "                        real_feature = mat2.to(device)\n",
    "                        fake_featuer = torch.rand(batch_size, d2).to(device)\n",
    "                        TP, TN, FP, FN = 0, 0, 0, 0\n",
    "\n",
    "                        with torch.no_grad():\n",
    "                            predict_real = D(real_feature)\n",
    "                            predict_fake = D(fake_feature)\n",
    "\n",
    "                        for e in predict_real:\n",
    "                            if e.item() > 0.5:\n",
    "                                TP += 1\n",
    "                            else:\n",
    "                                FN += 1\n",
    "                        for e in predict_fake:\n",
    "                            if e.item() < 0.5:\n",
    "                                TN += 1\n",
    "                            else:\n",
    "                                FP += 1\n",
    "\n",
    "\n",
    "                        output = G(mat1)\n",
    "                        output = (output + 1) / 2\n",
    "                        origin = (mat2 + 1) / 2\n",
    "                        print(f'hD: {hD}\\thG: {hG}\\tepoch: {num_epoch}\\tg_lr: {g_lr}\\td_lr: {d_lr}\\t', torch.mean(torch.pow(output[:,2:] - origin[:,2:], 2)).item())\n",
    "                        \n",
    "# print(((output + 1) / 2))\n",
    "# print((mat2 + 1) / 2)\n",
    "        \n",
    "# print(f'{TP}\\t{FN}\\n{FP}\\t{TN}')\n",
    "# accuracy = (TP + TN) / (TP + TN + FN + FP)\n",
    "# precision = TP / (TP + FP)\n",
    "# recall = TP / (TP + FN)\n",
    "# F1 = 2 * precision / (precision + recall)\n",
    "# print(f'{accuracy=}\\t{precision=}\\t{recall=}\\t{F1=}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "primary-angel",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = G(mat1)\n",
    "print((output + 1) / 2)\n",
    "print((mat2 + 1) / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "unnecessary-gauge",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "continued-officer",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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